SentenceTransformer based on anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-1e-200k

This is a sentence-transformers model finetuned from anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-1e-200k on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-1e-200k
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-3e-200k")
# Run inference
sentences = [
    'thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có yêu_cầu_là thẻ hướng_dẫn_viên du_lịch nội_địa',
    'thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có kết_quả_thực_hiện là gì ?',
    'cấp giấy_phép hoạt_động khám , chữa bệnh_nhân_đạo đối_với bệnh_viện trên địa_bàn quản_lý của sở y_tế ( trừ các bệnh_viện thuộc thẩm_quyền của bộ y_tế và bộ quốc_phòng ) và áp_dụng đối_với trường_hợp khi thay_đổi hình_thức tổ_chức , chia tách , hợp_nhất , sáp_nhập có phí , lệ_phí là bao_nhiêu ?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Datasets: dim_768 and dim_512
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512
cosine_accuracy@1	0.0823	0.0832
cosine_accuracy@3	0.2515	0.2492
cosine_accuracy@5	0.4185	0.4164
cosine_accuracy@10	0.8387	0.84
cosine_precision@1	0.0823	0.0832
cosine_precision@3	0.0838	0.0831
cosine_precision@5	0.0837	0.0833
cosine_precision@10	0.0839	0.084
cosine_recall@1	0.0823	0.0832
cosine_recall@3	0.2515	0.2492
cosine_recall@5	0.4185	0.4164
cosine_recall@10	0.8387	0.84
cosine_ndcg@10	0.3805	0.3808
cosine_mrr@10	0.2448	0.2449
cosine_map@100	0.2537	0.2537

Training Details

Training Dataset

json

Dataset: json
Size: 181,350 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 8 tokens
mean: 66.15 tokens
max: 256 tokens

min: 8 tokens
mean: 24.6 tokens
max: 256 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 66.15 tokens max: 256 tokens	min: 8 tokens mean: 24.6 tokens max: 256 tokens

Samples:

positive	anchor
`tính tiền cấp quyền khai_thác tài_nguyên nước đối_với công_trình chưa vận_hành cấp tỉnh có yêu_cầu_là .`	`tính tiền cấp quyền khai_thác tài_nguyên nước đối_với công_trình chưa vận_hành cấp tỉnh có yêu_cầu gì ?`
`xóa_đăng_ký hành_nghề và thu_hồi thẻ công_chứng_viên trường_hợp công_chứng_viên không còn hành_nghề tại tổ_chức hành_nghề công_chứng được thực_hiện mức_độ trực_tuyến toàn_trình`	`xóa_đăng_ký hành_nghề và thu_hồi thẻ công_chứng_viên trường_hợp công_chứng_viên không còn hành_nghề tại tổ_chức hành_nghề công_chứng được thực_hiện mức_độ mấy ?`
thủ_tục cấp giấy chứng_nhận đủ điều_kiện kinh_doanh hoạt_động thể_thao đối_với môn mô_tô nước trên biển có trình_tự thực_hiện như sau : doanh_nghiệp gửi hồ_sơ đến cơ_quan chuyên_môn về thể_dục , thể_thao thuộc ủy_ban nhân nhân cấp tỉnh nơi đăng_ký địa_điểm kinh_doanh hoạt_động thể_thao hoặc nơi doanh_nghiệp có trụ_sở chính trong trường_hợp_doanh_nghiệp có nhiều địa_điểm kinh_doanh hoạt_động thể_thao . cơ_quan chuyên_môn về thể_dục , thể_thao thuộc ủy_ban nhân_dân cấp tỉnh ( sau đây gọi là cơ_quan cấp giấy chứng_nhận đủ điều_kiện ) cấp cho doanh_nghiệp giấy tiếp_nhận hồ_sơ . trường_hợp hồ_sơ cần sửa_đổi , bổ_sung , cơ_quan cấp giấy chứng_nhận đủ điều_kiện thông_báo trực_tiếp hoặc bằng văn_bản những nội_dung cần sửa_đổi , bổ_sung đến doanh_nghiệp trong thời_hạn 03 ngày làm_việc , kể từ ngày nhận hồ_sơ .	`thủ_tục cấp giấy chứng_nhận đủ điều_kiện kinh_doanh hoạt_động thể_thao đối_với môn mô_tô nước trên biển có trình_tự thực_hiện như thế_nào ?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512
    ],
    "matryoshka_weights": [
        1,
        1
    ],
    "n_dims_per_step": -1
}

Evaluation Dataset

json

Dataset: json
Size: 20,150 evaluation samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 9 tokens
mean: 62.93 tokens
max: 256 tokens

min: 9 tokens
mean: 24.51 tokens
max: 127 tokens

	positive	anchor
type	string	string
details	min: 9 tokens mean: 62.93 tokens max: 256 tokens	min: 9 tokens mean: 24.51 tokens max: 127 tokens

Samples:

positive	anchor
điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004 có yêu_cầu hồ_sơ gồm : 1 . hồ_sơ do người sử_dụng đất nộp 01 bộ tại sở tài_nguyên và môi_trường gồm : văn_bản đề_nghị điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ ban_hành trước ngày 01 tháng 7 năm 2004 theo mẫu_số 03 ban_hành kèm theo thông_tư 33 / 2017 / tt - btnmtbản sao quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004bản sao giấy chứng_nhận quyền sử_dụng đất hoặc giấy chứng_nhận quyền sở_hữu nhà ở và quyền sử_dụng đất ở hoặc giấy chứng_nhận quyền sử_dụng đất , quyền sở_hữu nhà ở và tài_sản khác gắn liền với đất đã cấp ( nếu có ) bản_sao giấy_phép đầu_tư hoặc giấy chứng_nhận đầu_tư hoặc giấy đăng_ký kinh_doanh hoặc văn_bản chấp_thuận chủ_t...	`điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004 có yêu_cầu thành_phần hồ_sơ những gì ?`
`thủ_tục thông_báo hủy kết_quả phong phẩm hoặc suy cử chức_sắc đối_với các trường_hợp quy_định tại khoản 2 điều 33 của luật tín_ngưỡng , tôn_giáo được thực_hiện mức_độ trực_tuyến toàn_trình`	`thủ_tục thông_báo hủy kết_quả phong phẩm hoặc suy cử chức_sắc đối_với các trường_hợp quy_định tại khoản 2 điều 33 của luật tín_ngưỡng , tôn_giáo được thực_hiện mức_độ mấy ?`
`thủ_tục đính_chính giấy chứng_nhận đã cấp có phí , lệ_phí là : trực_tiếp :`	`thủ_tục đính_chính giấy chứng_nhận đã cấp có phí , lệ_phí là bao_nhiêu ?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512
    ],
    "matryoshka_weights": [
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
gradient_accumulation_steps: 2
learning_rate: 2e-05
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10
0	0	-	-	0.3307	0.3301
0.0002	10	0.0	-	-	-
0.0004	20	0.0	-	-	-
0.0007	30	0.0	-	-	-
0.0009	40	0.0	-	-	-
0.0011	50	0.0	-	-	-
0.0013	60	0.0	-	-	-
0.0015	70	0.0	-	-	-
0.0018	80	0.0	-	-	-
0.0020	90	0.0	-	-	-
0.0022	100	0.0	-	-	-
0.0024	110	0.0	-	-	-
0.0026	120	0.0	-	-	-
0.0029	130	0.0	-	-	-
0.0031	140	0.0	-	-	-
0.0033	150	0.0	-	-	-
0.0035	160	0.0	-	-	-
0.0037	170	0.0	-	-	-
0.0040	180	0.0	-	-	-
0.0042	190	0.0	-	-	-
0.0044	200	0.0	-	-	-
0.0046	210	0.0	-	-	-
0.0049	220	0.0	-	-	-
0.0051	230	0.0	-	-	-
0.0053	240	0.0	-	-	-
0.0055	250	0.0	-	-	-
0.0057	260	0.0	-	-	-
0.0060	270	0.0	-	-	-
0.0062	280	0.0	-	-	-
0.0064	290	0.0	-	-	-
0.0066	300	0.0	-	-	-
0.0068	310	0.0	-	-	-
0.0071	320	0.0	-	-	-
0.0073	330	0.0	-	-	-
0.0075	340	0.0	-	-	-
0.0077	350	0.0	-	-	-
0.0079	360	0.0	-	-	-
0.0082	370	0.0006	-	-	-
0.0084	380	0.0	-	-	-
0.0086	390	0.0	-	-	-
0.0088	400	0.0	-	-	-
0.0090	410	0.0	-	-	-
0.0093	420	0.0	-	-	-
0.0095	430	0.0	-	-	-
0.0097	440	0.0	-	-	-
0.0099	450	0.0	-	-	-
0.0101	460	0.0	-	-	-
0.0104	470	0.0	-	-	-
0.0106	480	0.0486	-	-	-
0.0108	490	0.0	-	-	-
0.0110	500	0.0	-	-	-
0.0112	510	0.0	-	-	-
0.0115	520	0.0	-	-	-
0.0117	530	0.0	-	-	-
0.0119	540	0.0	-	-	-
0.0121	550	0.0	-	-	-
0.0124	560	0.0	-	-	-
0.0126	570	0.0	-	-	-
0.0128	580	0.0	-	-	-
0.0130	590	0.0	-	-	-
0.0132	600	0.0	-	-	-
0.0135	610	0.0	-	-	-
0.0137	620	0.0	-	-	-
0.0139	630	0.0	-	-	-
0.0141	640	0.0	-	-	-
0.0143	650	0.0	-	-	-
0.0146	660	0.0	-	-	-
0.0148	670	0.0	-	-	-
0.0150	680	0.0	-	-	-
0.0152	690	0.0	-	-	-
0.0154	700	0.0	-	-	-
0.0157	710	0.0	-	-	-
0.0159	720	0.0	-	-	-
0.0161	730	0.0	-	-	-
0.0163	740	0.0	-	-	-
0.0165	750	0.0	-	-	-
0.0168	760	0.0	-	-	-
0.0170	770	0.0	-	-	-
0.0172	780	0.0	-	-	-
0.0174	790	0.0	-	-	-
0.0176	800	0.0	-	-	-
0.0179	810	0.0	-	-	-
0.0181	820	0.0002	-	-	-
0.0183	830	0.0002	-	-	-
0.0185	840	0.0	-	-	-
0.0187	850	0.0	-	-	-
0.0190	860	0.0	-	-	-
0.0192	870	0.0	-	-	-
0.0194	880	0.0	-	-	-
0.0196	890	0.0	-	-	-
0.0199	900	0.0	-	-	-
0.0201	910	0.0	-	-	-
0.0203	920	0.0	-	-	-
0.0205	930	0.0	-	-	-
0.0207	940	0.0	-	-	-
0.0210	950	0.0	-	-	-
0.0212	960	0.0	-	-	-
0.0214	970	0.0	-	-	-
0.0216	980	0.0	-	-	-
0.0218	990	0.0	-	-	-
0.0221	1000	0.0	-	-	-
0.0223	1010	0.0	-	-	-
0.0225	1020	0.0	-	-	-
0.0227	1030	0.0	-	-	-
0.0229	1040	0.0	-	-	-
0.0232	1050	0.0	-	-	-
0.0234	1060	0.0	-	-	-
0.0236	1070	0.0	-	-	-
0.0238	1080	0.0	-	-	-
0.0240	1090	0.0	-	-	-
0.0243	1100	0.0	-	-	-
0.0245	1110	0.0	-	-	-
0.0247	1120	0.0	-	-	-
0.0249	1130	0.0002	-	-	-
0.0251	1140	0.0	-	-	-
0.0254	1150	0.0	-	-	-
0.0256	1160	0.0	-	-	-
0.0258	1170	0.0	-	-	-
0.0260	1180	0.0	-	-	-
0.0262	1190	0.0	-	-	-
0.0265	1200	0.0	-	-	-
0.0267	1210	0.0	-	-	-
0.0269	1220	0.046	-	-	-
0.0271	1230	0.0	-	-	-
0.0274	1240	0.0	-	-	-
0.0276	1250	0.0	-	-	-
0.0278	1260	0.0	-	-	-
0.0280	1270	0.0	-	-	-
0.0282	1280	0.0	-	-	-
0.0285	1290	0.0	-	-	-
0.0287	1300	0.0	-	-	-
0.0289	1310	0.0	-	-	-
0.0291	1320	0.0	-	-	-
0.0293	1330	0.0	-	-	-
0.0296	1340	0.0	-	-	-
0.0298	1350	0.0	-	-	-
0.0300	1360	0.0	-	-	-
0.0302	1370	0.0	-	-	-
0.0304	1380	0.0	-	-	-
0.0307	1390	0.0	-	-	-
0.0309	1400	0.0	-	-	-
0.0311	1410	0.0	-	-	-
0.0313	1420	0.0	-	-	-
0.0315	1430	0.0	-	-	-
0.0318	1440	0.0	-	-	-
0.0320	1450	0.0	-	-	-
0.0322	1460	0.0	-	-	-
0.0324	1470	0.0	-	-	-
0.0326	1480	0.0	-	-	-
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0.1928	8740	0.0	-	-	-
0.1930	8750	0.0	-	-	-
0.1932	8760	0.0	-	-	-
0.1934	8770	0.0	-	-	-
0.1937	8780	0.0	-	-	-
0.1939	8790	0.0	-	-	-
0.1941	8800	0.0	-	-	-
0.1943	8810	0.0	-	-	-
0.1945	8820	0.0	-	-	-
0.1948	8830	0.0	-	-	-
0.1950	8840	0.0	-	-	-
0.1952	8850	0.0	-	-	-
0.1954	8860	0.0	-	-	-
0.1956	8870	0.0	-	-	-
0.1959	8880	0.0	-	-	-
0.1961	8890	0.0	-	-	-
0.1963	8900	0.0	-	-	-
0.1965	8910	0.0	-	-	-
0.1967	8920	0.0	-	-	-
0.1970	8930	0.0	-	-	-
0.1972	8940	0.0	-	-	-
0.1974	8950	0.0	-	-	-
0.1976	8960	0.0	-	-	-
0.1978	8970	0.0	-	-	-
0.1981	8980	0.0	-	-	-
0.1983	8990	0.0	-	-	-
0.1985	9000	0.0	-	-	-
0.1987	9010	0.0	-	-	-
0.1990	9020	0.0	-	-	-
0.1992	9030	0.0	-	-	-
0.1994	9040	0.0	-	-	-
0.1996	9050	0.0	-	-	-
0.1998	9060	0.0	-	-	-
0.2001	9070	0.0	-	-	-
0.2003	9080	0.0	-	-	-
0.2005	9090	0.0	-	-	-
0.2007	9100	0.0	-	-	-
0.2009	9110	0.0	-	-	-
0.2012	9120	0.0	-	-	-
0.2014	9130	0.0	-	-	-
0.2016	9140	0.0	-	-	-
0.2018	9150	0.0	-	-	-
0.2020	9160	0.0	-	-	-
0.2023	9170	0.0	-	-	-
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0.2078	9420	0.0001	-	-	-
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0.2164	9810	0.0001	-	-	-
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1.9189	87000	0.0337	-	-	-
1.9192	87010	0.0	-	-	-
1.9194	87020	0.0	-	-	-
1.9196	87030	0.0	-	-	-
1.9198	87040	0.0	-	-	-
1.9200	87050	0.0	-	-	-
1.9203	87060	0.0	-	-	-
1.9205	87070	0.0	-	-	-
1.9207	87080	0.0	-	-	-
1.9209	87090	0.0	-	-	-
1.9211	87100	0.0	-	-	-
1.9214	87110	0.0	-	-	-
1.9216	87120	0.0	-	-	-
1.9218	87130	0.0	-	-	-
1.9220	87140	0.0	-	-	-
1.9222	87150	0.0	-	-	-
1.9225	87160	0.0	-	-	-
1.9227	87170	0.0	-	-	-
1.9229	87180	0.0	-	-	-
1.9231	87190	0.0	-	-	-
1.9234	87200	0.0	-	-	-
1.9236	87210	0.0	-	-	-
1.9238	87220	0.0	-	-	-
1.9240	87230	0.0	-	-	-
1.9242	87240	0.0001	-	-	-
1.9245	87250	0.0	-	-	-
1.9247	87260	0.0	-	-	-
1.9249	87270	0.0	-	-	-
1.9251	87280	0.0	-	-	-
1.9253	87290	0.0	-	-	-
1.9256	87300	0.0	-	-	-
1.9258	87310	0.0	-	-	-
1.9260	87320	0.0	-	-	-
1.9262	87330	0.0	-	-	-
1.9264	87340	0.0	-	-	-
1.9267	87350	0.0	-	-	-
1.9269	87360	0.0	-	-	-
1.9271	87370	0.0	-	-	-
1.9273	87380	0.0	-	-	-
1.9275	87390	0.0	-	-	-
1.9278	87400	0.0	-	-	-
1.9280	87410	0.0	-	-	-
1.9282	87420	0.0	-	-	-
1.9284	87430	0.0	-	-	-
1.9286	87440	0.0	-	-	-
1.9289	87450	0.0	-	-	-
1.9291	87460	0.0	-	-	-
1.9293	87470	0.0	-	-	-
1.9295	87480	0.0	-	-	-
1.9297	87490	0.0	-	-	-
1.9300	87500	0.0	-	-	-
1.9302	87510	0.0	-	-	-
1.9304	87520	0.0001	-	-	-
1.9306	87530	0.0	-	-	-
1.9309	87540	0.0	-	-	-
1.9311	87550	0.0	-	-	-
1.9313	87560	0.0	-	-	-
1.9315	87570	0.0	-	-	-
1.9317	87580	0.0	-	-	-
1.9320	87590	0.0	-	-	-
1.9322	87600	0.0	-	-	-
1.9324	87610	0.0	-	-	-
1.9326	87620	0.0	-	-	-
1.9328	87630	0.0	-	-	-
1.9331	87640	0.0	-	-	-
1.9333	87650	0.0	-	-	-
1.9335	87660	0.0	-	-	-
1.9337	87670	0.0	-	-	-
1.9339	87680	0.0	-	-	-
1.9342	87690	0.0	-	-	-
1.9344	87700	0.0	-	-	-
1.9346	87710	0.0	-	-	-
1.9348	87720	0.0	-	-	-
1.9350	87730	0.0	-	-	-
1.9353	87740	0.0	-	-	-
1.9355	87750	0.0	-	-	-
1.9357	87760	0.0	-	-	-
1.9359	87770	0.0	-	-	-
1.9361	87780	0.0025	-	-	-
1.9364	87790	0.0	-	-	-
1.9366	87800	0.0	-	-	-
1.9368	87810	0.0	-	-	-
1.9370	87820	0.0004	-	-	-
1.9372	87830	0.0	-	-	-
1.9375	87840	0.0	-	-	-
1.9377	87850	0.0	-	-	-
1.9379	87860	0.0	-	-	-
1.9381	87870	0.0	-	-	-
1.9384	87880	0.0	-	-	-
1.9386	87890	0.0	-	-	-
1.9388	87900	0.0	-	-	-
1.9390	87910	0.0	-	-	-
1.9392	87920	0.0	-	-	-
1.9395	87930	0.0	-	-	-
1.9397	87940	0.0	-	-	-
1.9399	87950	0.0	-	-	-
1.9401	87960	0.0	-	-	-
1.9403	87970	0.0	-	-	-
1.9406	87980	0.0	-	-	-
1.9408	87990	0.0	-	-	-
1.9410	88000	0.0	-	-	-
1.9412	88010	0.0	-	-	-
1.9414	88020	0.0	-	-	-
1.9417	88030	0.0	-	-	-
1.9419	88040	0.0	-	-	-
1.9421	88050	0.0	-	-	-
1.9423	88060	0.0	-	-	-
1.9425	88070	0.0	-	-	-
1.9428	88080	0.0	-	-	-
1.9430	88090	0.0	-	-	-
1.9432	88100	0.0	-	-	-
1.9434	88110	0.0	-	-	-
1.9436	88120	0.0	-	-	-
1.9439	88130	0.0	-	-	-
1.9441	88140	0.0	-	-	-
1.9443	88150	0.0	-	-	-
1.9445	88160	0.0	-	-	-
1.9447	88170	0.0	-	-	-
1.9450	88180	0.0	-	-	-
1.9452	88190	0.0	-	-	-
1.9454	88200	0.0	-	-	-
1.9456	88210	0.0	-	-	-
1.9459	88220	0.0	-	-	-
1.9461	88230	0.0	-	-	-
1.9463	88240	0.0	-	-	-
1.9465	88250	0.0	-	-	-
1.9467	88260	0.0	-	-	-
1.9470	88270	0.0	-	-	-
1.9472	88280	0.0	-	-	-
1.9474	88290	0.0	-	-	-
1.9476	88300	0.0	-	-	-
1.9478	88310	0.0	-	-	-
1.9481	88320	0.0	-	-	-
1.9483	88330	0.0	-	-	-
1.9485	88340	0.0	-	-	-
1.9487	88350	0.0	-	-	-
1.9489	88360	0.0	-	-	-
1.9492	88370	0.0	-	-	-
1.9494	88380	0.0	-	-	-
1.9496	88390	0.0	-	-	-
1.9498	88400	0.0	-	-	-
1.9500	88410	0.0	-	-	-
1.9503	88420	0.0	-	-	-
1.9505	88430	0.0	-	-	-
1.9507	88440	0.0	-	-	-
1.9509	88450	0.0	-	-	-
1.9511	88460	0.0	-	-	-
1.9514	88470	0.0	-	-	-
1.9516	88480	0.0	-	-	-
1.9518	88490	0.0	-	-	-
1.9520	88500	0.0	-	-	-
1.9522	88510	0.0	-	-	-
1.9525	88520	0.0	-	-	-
1.9527	88530	0.0	-	-	-
1.9529	88540	0.0	-	-	-
1.9531	88550	0.0	-	-	-
1.9533	88560	0.0	-	-	-
1.9536	88570	0.0	-	-	-
1.9538	88580	0.0	-	-	-
1.9540	88590	0.0	-	-	-
1.9542	88600	0.0	-	-	-
1.9545	88610	0.0	-	-	-
1.9547	88620	0.0	-	-	-
1.9549	88630	0.0	-	-	-
1.9551	88640	0.0	-	-	-
1.9553	88650	0.0	-	-	-
1.9556	88660	0.0	-	-	-
1.9558	88670	0.0	-	-	-
1.9560	88680	0.0	-	-	-
1.9562	88690	0.0	-	-	-
1.9564	88700	0.0	-	-	-
1.9567	88710	0.0	-	-	-
1.9569	88720	0.0	-	-	-
1.9571	88730	0.0	-	-	-
1.9573	88740	0.0	-	-	-
1.9575	88750	0.0	-	-	-
1.9578	88760	0.0	-	-	-
1.9580	88770	0.0	-	-	-
1.9582	88780	0.0	-	-	-
1.9584	88790	0.0	-	-	-
1.9586	88800	0.0	-	-	-
1.9589	88810	0.0	-	-	-
1.9591	88820	0.0	-	-	-
1.9593	88830	0.0	-	-	-
1.9595	88840	0.0	-	-	-
1.9597	88850	0.0	-	-	-
1.9600	88860	0.0	-	-	-
1.9602	88870	0.0	-	-	-
1.9604	88880	0.0	-	-	-
1.9606	88890	0.0	-	-	-
1.9608	88900	0.0	-	-	-
1.9611	88910	0.0	-	-	-
1.9613	88920	0.0	-	-	-
1.9615	88930	0.0	-	-	-
1.9617	88940	0.0	-	-	-
1.9620	88950	0.0	-	-	-
1.9622	88960	0.0	-	-	-
1.9624	88970	0.0	-	-	-
1.9626	88980	0.0	-	-	-
1.9628	88990	0.0	-	-	-
1.9631	89000	0.0	-	-	-
1.9633	89010	0.0	-	-	-
1.9635	89020	0.0	-	-	-
1.9637	89030	0.0	-	-	-
1.9639	89040	0.0	-	-	-
1.9642	89050	0.0	-	-	-
1.9644	89060	0.0	-	-	-
1.9646	89070	0.0	-	-	-
1.9648	89080	0.0	-	-	-
1.9650	89090	0.0	-	-	-
1.9653	89100	0.0	-	-	-
1.9655	89110	0.0	-	-	-
1.9657	89120	0.0	-	-	-
1.9659	89130	0.0	-	-	-
1.9661	89140	0.0	-	-	-
1.9664	89150	0.0	-	-	-
1.9666	89160	0.0	-	-	-
1.9668	89170	0.0	-	-	-
1.9670	89180	0.0	-	-	-
1.9672	89190	0.0	-	-	-
1.9675	89200	0.0	-	-	-
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1.9679	89220	0.0	-	-	-
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1.9686	89250	0.0	-	-	-
1.9688	89260	0.0	-	-	-
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1.9692	89280	0.0	-	-	-
1.9695	89290	0.0	-	-	-
1.9697	89300	0.0	-	-	-
1.9699	89310	0.0	-	-	-
1.9701	89320	0.0	-	-	-
1.9703	89330	0.0	-	-	-
1.9706	89340	0.0	-	-	-
1.9708	89350	0.0	-	-	-
1.9710	89360	0.0	-	-	-
1.9712	89370	0.0	-	-	-
1.9714	89380	0.0	-	-	-
1.9717	89390	0.0	-	-	-
1.9719	89400	0.0	-	-	-
1.9721	89410	0.0	-	-	-
1.9723	89420	0.0	-	-	-
1.9725	89430	0.0	-	-	-
1.9728	89440	0.0	-	-	-
1.9730	89450	0.0	-	-	-
1.9732	89460	0.0	-	-	-
1.9734	89470	0.0	-	-	-
1.9736	89480	0.0	-	-	-
1.9739	89490	0.0	-	-	-
1.9741	89500	0.0	-	-	-
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1.9745	89520	0.0	-	-	-
1.9747	89530	0.0	-	-	-
1.9750	89540	0.0	-	-	-
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1.9754	89560	0.0	-	-	-
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1.9758	89580	0.0	-	-	-
1.9761	89590	0.0	-	-	-
1.9763	89600	0.0	-	-	-
1.9765	89610	0.0	-	-	-
1.9767	89620	0.0	-	-	-
1.9770	89630	0.0	-	-	-
1.9772	89640	0.0	-	-	-
1.9774	89650	0.0	-	-	-
1.9776	89660	0.0	-	-	-
1.9778	89670	0.0	-	-	-
1.9781	89680	0.0	-	-	-
1.9783	89690	0.0	-	-	-
1.9785	89700	0.0	-	-	-
1.9787	89710	0.0	-	-	-
1.9789	89720	0.0	-	-	-
1.9792	89730	0.0	-	-	-
1.9794	89740	0.0	-	-	-
1.9796	89750	0.0	-	-	-
1.9798	89760	0.0	-	-	-
1.9800	89770	0.0	-	-	-
1.9803	89780	0.0	-	-	-
1.9805	89790	0.0	-	-	-
1.9807	89800	0.0	-	-	-
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1.9811	89820	0.0	-	-	-
1.9814	89830	0.0	-	-	-
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1.9820	89860	0.0	-	-	-
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1.9825	89880	0.0	-	-	-
1.9827	89890	0.0	-	-	-
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1.9833	89920	0.0	-	-	-
1.9836	89930	0.0	-	-	-
1.9838	89940	0.0	-	-	-
1.9840	89950	0.0	-	-	-
1.9842	89960	0.0	-	-	-
1.9844	89970	0.0	-	-	-
1.9847	89980	0.0	-	-	-
1.9849	89990	0.0	-	-	-
1.9851	90000	0.0	-	-	-
1.9853	90010	0.0	-	-	-
1.9856	90020	0.0	-	-	-
1.9858	90030	0.0	-	-	-
1.9860	90040	0.0	-	-	-
1.9862	90050	0.0	-	-	-
1.9864	90060	0.0	-	-	-
1.9867	90070	0.0	-	-	-
1.9869	90080	0.0	-	-	-
1.9871	90090	0.0	-	-	-
1.9873	90100	0.0	-	-	-
1.9875	90110	0.0	-	-	-
1.9878	90120	0.0	-	-	-
1.9880	90130	0.0	-	-	-
1.9882	90140	0.0	-	-	-
1.9884	90150	0.0	-	-	-
1.9886	90160	0.0	-	-	-
1.9889	90170	0.0	-	-	-
1.9891	90180	0.0	-	-	-
1.9893	90190	0.0	-	-	-
1.9895	90200	0.0	-	-	-
1.9897	90210	0.0	-	-	-
1.9900	90220	0.0	-	-	-
1.9902	90230	0.0	-	-	-
1.9904	90240	0.0	-	-	-
1.9906	90250	0.0	-	-	-
1.9908	90260	0.0	-	-	-
1.9911	90270	0.0	-	-	-
1.9913	90280	0.0	-	-	-
1.9915	90290	0.0	-	-	-
1.9917	90300	0.0	-	-	-
1.9919	90310	0.0	-	-	-
1.9922	90320	0.0	-	-	-
1.9924	90330	0.0	-	-	-
1.9926	90340	0.0	-	-	-
1.9928	90350	0.0	-	-	-
1.9931	90360	0.0	-	-	-
1.9933	90370	0.0	-	-	-
1.9935	90380	0.0	-	-	-
1.9937	90390	0.0	-	-	-
1.9939	90400	0.0	-	-	-
1.9942	90410	0.0	-	-	-
1.9944	90420	0.0	-	-	-
1.9946	90430	0.0	-	-	-
1.9948	90440	0.0	-	-	-
1.9950	90450	0.0	-	-	-
1.9953	90460	0.0	-	-	-
1.9955	90470	0.0	-	-	-
1.9957	90480	0.0	-	-	-
1.9959	90490	0.0	-	-	-
1.9961	90500	0.0	-	-	-
1.9964	90510	0.0	-	-	-
1.9966	90520	0.0	-	-	-
1.9968	90530	0.0	-	-	-
1.9970	90540	0.0	-	-	-
1.9972	90550	0.0	-	-	-
1.9975	90560	0.0	-	-	-
1.9977	90570	0.0	-	-	-
1.9979	90580	0.0	-	-	-
1.9981	90590	0.0	-	-	-
1.9983	90600	0.0	-	-	-
1.9986	90610	0.0	-	-	-
1.9988	90620	0.0	-	-	-
1.9990	90630	0.0	-	-	-
1.9992	90640	0.0	-	-	-
1.9994	90650	0.0	-	-	-
1.9997	90660	0.0	-	-	-
1.9999	90670	0.0	-	-	-
2.0	90674	-	0.0003	0.3805	0.3808

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.3.1
Transformers: 4.41.2
PyTorch: 2.1.2
Accelerate: 0.29.3
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Downloads last month: 2

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-3e-200k

Base model

bkai-foundation-models/vietnamese-bi-encoder

Finetuned

anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-1e-200k

Finetuned

(1)

this model

Papers for anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-3e-200k

Evaluation results

Cosine Accuracy@1 on dim 768
self-reported

0.082
Cosine Accuracy@3 on dim 768
self-reported

0.251
Cosine Accuracy@5 on dim 768
self-reported

0.418
Cosine Accuracy@10 on dim 768
self-reported

0.839
Cosine Precision@1 on dim 768
self-reported

0.082
Cosine Precision@3 on dim 768
self-reported

0.084
Cosine Precision@5 on dim 768
self-reported

0.084
Cosine Precision@10 on dim 768
self-reported

0.084
Cosine Recall@1 on dim 768
self-reported

0.082
Cosine Recall@3 on dim 768
self-reported

0.251
Cosine Recall@5 on dim 768
self-reported

0.418
Cosine Recall@10 on dim 768
self-reported

0.839
Cosine Ndcg@10 on dim 768
self-reported

0.380
Cosine Mrr@10 on dim 768
self-reported

0.245
Cosine Map@100 on dim 768
self-reported

0.254
Cosine Accuracy@1 on dim 512
self-reported

0.083
Cosine Accuracy@3 on dim 512
self-reported

0.249
Cosine Accuracy@5 on dim 512
self-reported

0.416
Cosine Accuracy@10 on dim 512
self-reported

0.840
Cosine Precision@1 on dim 512
self-reported

0.083